Question 1

Which of the following machine learning algorithms typically uses bagging?

A. IGradient boosted trees
B. K-means
C. Random forest
D. Decision tree

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Question 2

The implementation of linear regression in Spark ML first attempts to solve the linear regressionproblem using matrix decomposition, but this method does not scale well to large datasets with alarge number of variables.Which of the following approaches does Spark ML use to distribute the training of a linear regressionmodel for large data?

A. Logistic regression
B. Singular value decomposition
C. Iterative optimization

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Question 3

A data scientist has produced three new models for a single machine learning problem. In the past,the solution used just one model. All four models have nearly the same prediction latency, but amachine learning engineer suggests that the new solution will be less time efficient during inference.In which situation will the machine learning engineer be correct?

A. When the new solution requires if-else logic determining which model to use to compute eachprediction
B. When the new solution's models have an average latency that is larger than the size of theoriginal model
C. When the new solution requires the use of fewer feature variables than the original model
D. When the new solution requires that each model computes a prediction for every record
E. When the new solution's models have an average size that is larger than the size of the originalmodel

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Question 4

A data scientist has developed a machine learning pipeline with a static input data set using SparkML, but the pipeline is taking too long to process. They increase the number of workers in the clusterto get the pipeline to run more efficiently. They notice that the number of rows in the training setafter reconfiguring the cluster is different from the number of rows in the training set prior toreconfiguring the cluster.Which of the following approaches will guarantee a reproducible training and test set for eachmodel?

A. Manually configure the cluster
B. Write out the split data sets to persistent storage
C. Set a speed in the data splitting operation
D. Manually partition the input data

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Question 5

A data scientist is developing a single-node machine learning model. They have a large number ofmodel configurations to test as a part of their experiment. As a result, the model tuning processtakes too long to complete. Which of the following approaches can be used to speed up the modeltuning process?

A. Implement MLflow Experiment Tracking
B. Scale up with Spark ML
C. Enable autoscaling clusters
D. Parallelize with Hyperopt

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Question 6

A machine learning engineer is trying to scale a machine learning pipeline by distributing its singlenodemodel tuning process. After broadcasting the entire training data onto each core, each core inthe cluster can train one model at a time. Because the tuning process is still running slowly, theengineer wants to increase the level of parallelism from 4 cores to 8 cores to speed up the tuningprocess. Unfortunately, the total memory in the cluster cannot be increased.In which of the following scenarios will increasing the level of parallelism from 4 to 8 speed up thetuning process?

A. When the tuning process in randomized
B. When the entire data can fit on each core
C. When the model is unable to be parallelized
D. When the data is particularly long in shape
E. When the data is particularly wide in shape

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Question 7

A data scientist has been given an incomplete notebook from the data engineering team. Thenotebook uses a Spark DataFrame spark_df on which the data scientist needs to perform furtherfeature engineering. Unfortunately, the data scientist has not yet learned the PySpark DataFrameAPI.Which of the following blocks of code can the data scientist run to be able to use the pandas API onSpark?

A. import pyspark.pandas as psdf = ps.DataFrame(spark_df)
B. import pyspark.pandas as psdf = ps.to_pandas(spark_df)
C. spark_df.to_pandas()
D. import pandas as pddf = pd.DataFrame(spark_df)

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Question 8

Which of the following describes the relationship between native Spark DataFrames and pandas APIon Spark DataFrames?

A. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additionalmetadata
B. pandas API on Spark DataFrames are more performant than Spark DataFrames
C. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
D. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames

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Question 9

Which statement describes a Spark ML transformer?

A. A transformer is an algorithm which can transform one DataFrame into another DataFrame
B. A transformer is a hyperparameter grid that can be used to train a model
C. A transformer chains multiple algorithms together to transform an ML workflow
D. A transformer is a learning algorithm that can use a DataFrame to train a model

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Question 10

Which of the following tools can be used to distribute large-scale feature engineering without theuse of a UDF or pandas Function API for machine learning pipelines?

A. Keras
B. Scikit-learn
C. PyTorch
D. Spark ML

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